HuatuoGPT is an open-source large language model specifically designed for medical consultation scenarios, primarily targeting Chinese users. It aims to provide accessible, doctor-like advice for common medical queries, thereby alleviating pressure on healthcare professionals and offering a valuable resource for public health information.

How It Works

HuatuoGPT is built upon existing LLMs (Baichuan-7B and Ziya-LLaMA-13B) and fine-tuned using a hybrid approach. This involves a large dataset combining distilled instructions and conversations from ChatGPT with real-world medical data from doctors. This dual-source strategy aims to imbue the model with both the structured knowledge of AI-generated data and the nuanced, practical insights of human medical professionals.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Requires model weights (e.g., LLaMA-13B for HuatuoGPT-13B) and the provided delta weights.
• Demo: Available at https://www.huatuogpt.cn/

Highlighted Details

• Achieved state-of-the-art in Chinese medical applications with HuatuoGPT-II.
• Paper accepted for EMNLP 2023.
• Offers 7B and 13B parameter models.
• Provides a comprehensive medical evaluation benchmark and dataset.

Maintenance & Community

• Developed by researchers from The Chinese University of Hong Kong, Shenzhen and the Shenzhen Research Institute of Big Data.
• Active development with releases in 2023, including model weights, data, and a tech report.

Licensing & Compatibility

• The project's specific license is not explicitly stated in the README, but it relies on LLaMA and Baichuan base models, which have their own licensing terms. The release of delta weights for LLaMA-based models suggests potential restrictions on commercial use or redistribution of the full model.

Limitations & Caveats

The model is intended for quick consultations, not as a replacement for doctors. It carries risks of misunderstanding medical jargon, generating inaccurate "hallucinations," and potentially perpetuating biases present in its training data. Thorough evaluation is recommended before clinical application.